NSF INCLUDES: Mississippi Alliance for Women in Computing (MAWC)

密西西比妇女计算联盟（MAWC）项目将确定影响和激励密西西比的女学生和非裔美国女学生注册并坚持本科工程或科学计算专业的因素。 有一个特别需要的节目，是包括妇女和妇女的颜色谁是来自美国南部地区。这些学生通常很少参加鼓励计算的课外活动，也不太可能想象自己在计算机专业或职业中的样子。这项拟议的研究是为了帮助女孩知道计算机科学的存在，以及计算机科学学位可以提供哪些计算机科学工作。密西西比有一个丰富的环境，可以建立一个专注于建立课外和导师机会的联盟。 一个可扩展的管道模型，可扩展到南方联盟的妇女在计算（SAWC），将开发有三个主要目标：吸引妇女和妇女的颜色计算，以提高妇女在本科计算专业的保留率，并帮助中学后妇女过渡到计算劳动力。支持这些目标的活动包括：扩大密西西比国家妇女和信息技术中心的计算抱负奖计划，扩大奖学金的抱负获奖者，扩大学生主导的计算外展计划，建立一个密西西比黑人女孩代码章，通知和合作的计算机科学为密西西比倡议，为计算机专业的妇女建立一个夏桥和生活学习社区，并通过会议、午餐和学习会议、工作跟踪和实习，增加计算机专业妇女的专业发展机会。该项目将分析MAWC的课外活动是否会导致计算自我效能，并最终导致女性学生选择追求并坚持计算机专业和职业。 为了了解学生的参与和效能的变化，本研究的数据收集将通过人口和背景调查管理的妇女进入本科工程或科学为基础的计算专业在密西西比大学和学生的调查和评估MAWC赞助的计划。 使用歧视分析方法，要解决的具体研究问题是：1）哪些大学预科的经验影响了他们入学，2）哪些利益相关者影响了这些女孩在他们的决策过程中，和3）什么样的程序是有效的影响他们在主要的持久性。每个研究问题的预测变量是：大学前的经验，利益相关者和程序。结果变量为：（a）未参与MAWC方案编制的女本科生，（B）MAWC活动参与者，或（c）获得学士学位的MAWC参与者？STEM专业的学位。结果将补充出版的纵向研究的性别和种族层面的计算识字收购密西西比以及有效的CS角色模型编程的研究。

The Mississippi Alliance for Women in Computing (MAWC) project will identify factors that influence and motivate female students and female African American students in Mississippi to enroll and persist in an undergraduate engineering- or science-based computing major. There is a particular need for programming that is inclusive of women and women of color who are from the southern region of the United States. These students typically have less access to extracurricular activities that encourage computing, and are less likely to visualize themselves in a computing major or career. This proposed research is to help girls to know that computer science exists and what jobs in computer science are available with a degree in computer science. A rich environment exists in Mississippi for an alliance focused on building co-curricular and mentorship opportunities. A scalable pipeline model, expandable to a Southern Alliance for Women in Computing (SAWC), will be developed with three major objectives: to attract women and women of color to computing, to improve retention rates of women in undergraduate computing majors, and to help postsecondary women make the transition to the computing workforce. Activities to support these objectives include: scaling the National Center for Women and Information Technology Aspirations in Computing award program in Mississippi, expanding scholarships for Aspirations winners, expanding student-led computing outreach programs, establishing a Mississippi Black Girls Code chapter, informing and collaborating with the Computer Science for Mississippi initiative, creating a summer bridge and living-learning community for women in computing majors, and increasing professional development opportunities for women in computing through conferences, lunch and learn meetings, job shadowing, and internships. The project will analyze whether the co-curricular activities of MAWC lead to computing self-efficacy and ultimately female students selecting to pursue and persist in computing majors and careers. In order to understand student participation and efficacy changes, data collection for this research will be through demographic and background surveys administered to women entering an undergraduate engineering- or science-based computing major at a university in Mississippi and student surveys and evaluations in MAWC-sponsored programs. Using discriminate analysis methods, specific research questions to be addressed are: 1) Which pre-collegiate experiences influenced them to enroll, 2) Which stakeholders influenced these girls in their decision-making process, and 3) What programs are effective in impacting their persistence in the major. Predictor variables for each respective research question are: pre-collegiate experiences, stakeholders, and programs. Outcome variables are: (a) a female undergraduate student with no involvement with MAWC programming, (b) MAWC activity participant, or (c) a MAWC participant having graduated with a bachelor?s degree in a STEM major. Results will complement published longitudinal research on the gendered and raced dimensions of computing literacy acquisition in Mississippi as well as research on effective CS role model programming.